Kongish Daily, a Facebook page promoting Kongish – a creative, critical, and colloquial form of Hong Kong English with Cantonese inflections – has attracted a following in social media over the past decade. It has also sparked interest among sociolinguists interested in (post-)multilingual developments in East Asia. This study is built on Hansen Edwards’s (2016) premise that Hong Kong English would gain wider acceptance in Hong Kong as the cultural identity of local language users shifted amidst sociocultural transformations. We first provide an overview of the Kongish phenomenon, followed by a qualitative study involving 30 active Kongish users from diverse age groups, genders and occupations. Through semi-structured interviews, we explore users’ perceptions of language and identity. Our findings support Hansen Edwards’s prediction regarding the strengthening of Hong Kongers’ cultural identification, while revealing an evolving, counter-stereotypical Hong Kong culture as well as an opinion divide on the future trajectory of Kongish.

Unbalanced bilinguals often exhibit reduced emotionality in their non-native language, although the underlying neural mechanisms remain poorly understood. This fMRI (functional magnetic resonance imaging) study investigated neural differences during a silent reading task where late Spanish–English bilinguals read happy, fearful and neutral fiction passages in their first (L1) and second (L2) languages. We observed a significant language-by-emotionality interaction in the left hippocampus while participants read fearful texts, indicating a stronger limbic system response in L1. Functional connectivity analyses revealed lower coupling between semantic (left anterior temporal lobe) and limbic (left amygdala) regions when reading fearful texts in L2, suggesting less integrated emotional processing. Overall, these findings show that emotional reading in unbalanced bilinguals is strongly influenced by language, with a higher emotional response and more integrated connectivity between semantic and affective areas in the native language.

This article explores recent developments in English language education in Taiwan, situating them within broader sociopolitical shifts and demographic changes. It begins by outlining the emergence of English in Taiwan’s education system before presenting the formulation and objectives of the Bilingual 2030 policy. The article then analyzes how this national initiative has reshaped English language education across educational levels. In particular, it highlights the expansion of English–medium instruction (EMI) in higher education as a central feature of Taiwan’s bilingualization agenda. Finally, it discusses two key developments influenced by the policy: the internationalization of higher education as a response to demographic and policy pressures, and the resulting expansion of EMI courses as a central strategy for attracting global talent and sustaining university enrollment.

Against the proliferation of large language model (LLM) based Artificial Intelligence (AI) products such as ChatGPT and Gemini, and their increasing use in professional communication training, researchers, including applied linguists, have cautioned that these products (re)produce cultural stereotypes due to their training data. However, there is a limited understanding of how humans navigate the assumptions and biases present in the responses of these LLM-powered systems and the role humans play in perpetuating stereotypes during interactions with LLMs. In this article, we use Sequential-Categorial Analysis, which combines Conversation Analysis and Membership Categorization Analysis, to analyze simulated interactions between a human physiotherapist and three LLM-powered chatbot patients of Chinese, Australian, and Indian cultural backgrounds. Coupled with analysis of information elicited from LLM chatbots and the human physiotherapist after each interaction, we demonstrate that users of LLM-powered systems are highly susceptible to becoming interactionally entrenched in culturally essentialized narratives. We use the concepts of interactional instinct and interactional entrenchment to argue that whilst human–AI interaction may be instinctively prosocial, LLM users need to develop Critical Interactional Competence for human–AI interaction through appropriate and targeted training and intervention, especially when LLM-powered tools are used in professional communication training programs.

We investigated whether executive functions (EFs) are engaged in bilingual language control in Finnish speakers with different degrees of Swedish language experience and proficiency, including early bilinguals, late high-proficiency bilinguals and low-proficiency learners of Swedish. In an online experiment, language switching was measured with a cued naming (CN) paradigm, and a Simon task was used to assess EF performance. Following the skill-learning (task specificity) hypothesis, we expected that language switching may be automatized and no longer rely on EFs in bilinguals with high language proficiency, but not for those with lower proficiency. Thus, we expected significant associations between the tasks in the lower proficiency participants only. Our results showed no CN switching–EF associations in the more experienced L2 speakers, but a significant association in lower-proficiency participants. This suggests that language switching engages EFs only in participants with lower proficiency in whom these processes are not yet automatized.

The present paper provides a small–scale exploratory analysis of L2 English pronunciation and accent aims among secondary school students in Germany – with a focus on the bath and lot vowels, rhoticity, and T–flapping. The eight learners investigated in the current study show blended use of Standard Southern British English (StSBrE) and Standard American (StAmE) phonological variants with relatively high degrees of variation between learners. StSBrE–oriented productions were dominant overall. Agreement of accent aim and L2 pronunciation was largely feature–dependent and limited overall but varied between learners.